featherless-ai/simple-jev

Use compatible open models from huggingface, for structured classification and scoring, without training a separate classifier head. Explore the demos, playground, and documentation at simple-jev.featherless.ai. Send shared context and a set of questions. Simple Jev reads the model's next-token logits for each question and builds a JSON response containing choices, rubric scores, or truth/support judgments. The model does not generate a JSON completion: the server constructs the response from the scores. The current implementation runs locally with Hugging Face Transformers and PyTorch. Shared request validation, versioned prompt instructions, and response scoring live in the plain Python common/ folder so other inference implementations can use the same rules. Try the public demo API with no login, API key, or authentication required. The demo has a 2k-token context limit and is rate limited to 2 requests per second (2 RPS). First, list the available models with GET /v1/models: The demo already serves…